Institutions that have relied on traditional channels to build and grow personal relationships face a future in which conversations and in-person meetings have become the equivalent of landline phones and cursive writing.

If you never meet the customer, how do you get to know them? Just as you trained employees to ask the right questions and read body language, you need to learn to decipher the digital footprints that your customers leave behind. By applying advanced analytics, you can predict customer behavior and deliver a highly personalized communication or offer at the moment when the customer is most receptive to receiving it.

A bank with 40 million customers would gain $485 million in incremental revenue by improving its customer experience rating from below average to above average.

Analyst firm CEB TowerGroup estimates that 75 percent of US banking transactions in 2012 came from digital channels. By 2016, that percent is expected to rise to 84 percent. Today, online transaction volumes are more than double that of branch activity. Bank employees have fewer opportunities to say, “Is there anything more we can do for you?”

Yet, customer service is critical - whether customers are in front of you or at their computers. A 2012 Forrester Research report, The Business Impact of Customer Service, calculates that a bank with 40 million customers would gain $485 million in incremental revenue by improving its customer experience rating from below average to above average.

Big data vs. small data

Finding a way to personalize offers involves making sound decisions on which data is meaningful, and in what context. Customer analytics tends to get conflated with the phrase “big data,” but success is just as dependent on “small data.” What’s the difference? Small data is knowing something specific about your customer, such as he just deposited a check much larger than ever before. Big data crunches information to compare a customer’s transactions and demographics against similar customers to segment that customer for marketing offers. Both involve analytics, but not in the same way.

Finding a starting point

Being able to segment customers is a critical starting point, the first stop in ending the “one size fits all” approach to customer communication. Your analysis needs to review customer behavior data to develop insight into how they will respond going forward. No two institutions will have the same segments, and savvy institutions are constantly honing and reworking segments as new information becomes available. Offer-response rates can be looped back into model building. Increasingly, understanding online behavior (how a customer moves through a website or app) is critical to successful segmentation.

Laying in optimization and real-time strategies

Let’s think about the customer who made that huge deposit. You want to get the right offer in front of him before he transfers that money into an investment account at your competitor. If your analytic efforts surface that information about the large deposit a month or a week later, it might be too late. This is where it becomes critical to optimize the process and use real-time monitoring. Optimization does more than create the right offer and the right means to get it in front of the customer. It calculates the opportunity cost of not making the offer. Not every big deposit should trigger a communication.

The real-time component makes sure that if an offer is necessary, it happens as quickly as possible and while the customer is still in-session. And the offer should be tailored to what she’s done or liked in the past. If one customer responds to a direct email or text message, that might be the approach to take. For another customer that tends to engage when she goes to do something (check her balance online), she might see a pop-up ad while checking the balance.

Customers like feeling that you ‘get them’

Some institutions are concerned that customers will find this kind of marketing intrusive - even if they would have no problem training a teller to ask that same customer about opening a CD or handing them a brochure about wealth management services. The reality is quite different. Despite privacy concerns, customers expect that you will know them: 60 percent said so in a 2013 SAS survey of 1,260 US respondents. They want recommendations for products and services based on their lifestyle, previous purchases and search history. Years of shopping on sites like Amazon and Zappos that offer personalized recommendations based on search patterns condition customers to expect this. This level of personalization is becoming a factor in how customers view service quality. Online retailers get high ratings, where industries that typically shy away from customized offers fare worse.

From conceptualization to reality

Being able to optimize and place offers in real time involves a few easily overcome technology hurdles. Banking has not invested in segmentation and personalization to the degree online retailers have. The first step is to recognize that if your customer analytics doesn’t monitor digital paths and can’t be done with optimization or real-time options, it won’t be effective. Many banks use analytics providers that offer a report on overall website activity, not the kind of personalized data that can be read as it streams in to the site. Website tags that allow institutions to track how a customer interacts with a digital channel haven’t been readily available in the past. Additionally, all of this data is useless if you can’t merge the online and offline data to build the profile that allows you to know what offer to make.

Luckily, banks don’t need to create this from scratch. Customer experience personalization solutions exist that can help by:

Capturing the complete online behavior of the customer and accurately associating it with data from other sources (including transactions).

Matching this behavior to a library of offers, reworking what is stocked on the library “shelves” as you see what clients respond to.

Striving to constantly deliver the best offer, not just any offer.

These types of solutions offer solid payoffs. Among the successes:

A global retail bank has increased target audience members by 500 percent at the same time it cuts its online media spending by 10 percent.

A global insurer doubled customer engagements in three months.

More targeted campaigns and improved customer service helped a North American bank achieve annual growth of 80,000 - 100,000 more incremental accounts than what the bank would gain by simply waiting for customers to come through the door.